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Magnetic recording at the highest areal density on conventional granular recording media uses $\sim 10$ grains to carry each bit of customer data. Efforts to reduce this number have focused on improving the resolution of the writing and reading processes and, in particular, on improving uniformity of the recording medium and on incorporating knowledge of the recording medium statistics into the detection process. This paper considers the extreme case of data detection on a perfect hexagonal array of grains. This paper looks at two aspects: 1) the estimation of the angle and positioning of the array with respect to the bit-steam and 2) the use of that information to assist data detection. At aggressive densities with very few grains per bit (GPB), the estimation proceeds well and the extra information is helpful in detection. With more GPB, the gains become small. [ABSTRACT FROM AUTHOR]

This paper introduces a weighted total least squares (WTLS)-based estimator into image registration to deal with the coordinates of control points (CPs) that are of unequal accuracy. The performance of the estimator is investigated by means of simulation experiments using different coordinate errors. Comparisons with ordinary least squares (LS), total LS (TLS), scaled TLS, and weighted LS estimators are made. A novel adaptive weight determination scheme is applied to experiments with remotely sensed images. These illustrate the practicability and effectiveness of the proposed registration method by collecting CPs with different-sized errors from multiple reference images with different spatial resolutions. This paper concludes that the WTLS-based iteratively reweighted TLS method achieves a more robust estimation of model parameters and higher registration accuracy if heteroscedastic errors occur in both the coordinates of reference CPs and target CPs. [ABSTRACT FROM PUBLISHER]

In this paper, we describe new lower bounds on error variance of phase offset estimation schemes used in IEEE 1588 based synchronization. The motivation for this study is to determine the feasibility of providing microsecond-level time synchronization over mobile backhaul networks for the backend in 4G cellular systems. Many packet filtering/selection techniques for phase offset estimation have been proposed in synchronization literature, however, lower limits on the performance of such estimators have not yet been described. In this paper, we re-derive two Bayesian estimation bounds, namely the Ziv-Zakai and Weiss-Weinstien bounds, for use under a non-Bayesian formulation. This enables us to apply these bounds to the problem of phase offset estimation. Simulation results compare the performance of existing estimation schemes against these lower bounds under a variety of different network scenarios. [ABSTRACT FROM AUTHOR]

The problem of adaptive noisy clustering is investigated. Given a set of noisy observations \(Z_{i}=X_{i}+\epsilon _{i}\) , \(i=1, \ldots , n\) , the goal is to design clusters associated with the law of \(X_{i}\) ’s, with unknown density \(f\) with respect to the Lebesgue measure. Since we observe a corrupted sample, a direct approach as the popular \(k\) -means is not suitable in this case. In this paper, we propose a noisy \(k\) -means minimization, which is based on the \(k\) -means loss function and a deconvolution estimator of the density \(f\) . In particular, this approach suffers from the dependence on a bandwidth involved in the deconvolution kernel. Fast rates of convergence for the excess risk are proposed for a particular choice of the bandwidth, which depends on the smoothness of the density \(f\) . Then, we turn out into the main issue of this paper: the data-driven choice of the bandwidth. We state an adaptive upper bound using a modified version of Lespki’s method, called Empirical Risk Comparison, where empirical risks associated with different bandwidths are compared. Eventually, we illustrate that the selection rule can be used in many statistical problems of \(M\) -estimation where the empirical risk depends on a nuisance parameter. [ABSTRACT FROM PUBLISHER]

Efficient operation of the integrated optimization or automation system in an industrial plant depends mainly on good measurement of product quality. However, measuring or estimating the product quality online in many industrial plants is usually not feasible using the available techniques. In this paper, a data-driven soft-sensor using case-based reasoning (CBR) and fuzzy-similarity rough sets is proposed for product quality estimation. Owning to the sustained learning ability, the modeling of a CBR soft-sensor does not need any additional model correction which is otherwise required by the neural network based methods to overcome the slow time-varying nature of industrial processes. Because the conventional k-nearest neighbor (k-NN) algorithm is strongly influenced by the value of k, an improved k-NN algorithm with dynamic adjustment of case similarity threshold is proposed to retrieve sufficient matching cases for making a correct estimation. Moreover, considering that the estimation accuracy of the CBR soft-sensor system is closely related to the weights of case feature, a feature weighting algorithm using fuzzy-similarity rough sets is proposed in this paper. This feature weighting method does not require any transcendental knowledge, and its computation complexity is only linear with respect to the number of cases and attributes. The developed soft-sensor system has been successfully applied in a large grinding plant in China. And the application results show that the system has achieved satisfactory estimation accuracy and adaptation ability. [ABSTRACT FROM AUTHOR]

In this paper, we contribute to answer the question: How accurately can we estimate heights of buildings with flat roofs given one high-resolution single-pass interferometric synthetic aperture radar (InSAR) image pair and one aerial orthophoto? What makes this problem challenging are the different sensor geometries and the sound stochastic combination of all available elevation cues. We revisit already existing methods and develop novel approaches to determine building heights. A rigorous stochastic approach based on least squares adjustment with functionally dependent parameters is introduced to combine all height measurements per building to one robust height estimate. Observation accuracies of the stochastic model are either taken from the literature or estimated empirically. A major benefit of adjustment is that it delivers a posterior standard deviation per height, which can be interpreted as a precision indicator and is of high relevance for practical applications. Estimated heights of an urban scene are compared to ground truth acquired with airborne laser scanning, allowing us to assess height accuracies that can be achieved under nearly optimal conditions. We conduct statistical tests that validate our model and show that a weighted combination of optical and synthetic aperture radar (SAR) data with least squares adjustment delivers reliable height estimates with meter accuracy for flat-roofed buildings. Additionally, we empirically estimate a confidence interval of the estimated heights that directly tells the user the security margin to be included, for example, in case of building evacuations for an anticipated flooding event, under the condition that the data and model have the same specifications as in this paper. [ABSTRACT FROM PUBLISHER]

This paper studies the problem of distributed parameter estimation in multiagent networks with exponential family observation statistics. A certainty-equivalence type distributed estimator of the consensus-plus-innovations form is proposed in which, at each observation sampling epoch, agents update their local parameter estimates by appropriately combining the data received from their neighbors and the locally sensed new information (innovation). Under global observability of the networked sensing model, i.e., the ability to distinguish between different instances of the parameter value based on the joint observation statistics, and mean connectivity of the inter-agent communication network, the proposed estimator is shown to yield consistent parameter estimates at each network agent. Further, it is shown that the distributed estimator is asymptotically efficient, in that, the asymptotic covariances of the agent estimates coincide with that of the optimal centralized estimator, i.e., the inverse of the centralized Fisher information rate. From a technical viewpoint, the proposed distributed estimator leads to non-Markovian mixed time-scale stochastic recursions and the analytical methods developed in this paper contribute to the general theory of distributed stochastic approximation. [ABSTRACT FROM PUBLISHER]

In this paper, a fast initial speed estimation method has been proposed for low rotational speed operation of speed sensorless induction motor drives based on the second-order differential of the secondary flux. The fast estimation can reduce an undesirable disturbance torque generation during the restart process. The effectiveness of the proposed method is confirmed by experiments. The estimation time has been compared with those in the literature and the benefit of the proposed method is confirmed. [ABSTRACT FROM AUTHOR]

This paper focuses on the linear optimal recursive sequential fusion filter design for multisensor systems subject to stochastic parameter perturbations, fading measurements, and correlated noises. The stochastic parameter perturbations existing in the state model are described by white multiplicative noises. The fading measurement phenomena for different sensors are described by independent random variables with known statistical properties. Moreover, the measurement noises of different sensors are correlated with each other and also correlated with the system noise at the same time step. First, a model equivalent to the original system is established by transferring the multiplicative noises into the additive noises. Then, based on the equivalent model and an innovation analysis method, a sequential fusion filter in the linear minimum variance sense is proposed to solve the linear optimal state estimation problem in real time according to the arriving order of measurements from different sensors. Finally, the equivalence on estimation accuracy of the proposed sequential fusion filter and the centralized fusion filter is strictly proven, which shows the optimality of the proposed sequential fusion algorithm. Moreover, the proposed sequential fusion filter has a reduced computational burden. Compared with the distributed matrix-weighted fusion filter, the computation of cross-covariance matrices is avoided and the estimation accuracy is improved. Finally, a simulation example verifies the effectiveness of the proposed sequential fusion filtering algorithm. [ABSTRACT FROM AUTHOR]

Time Difference of Arrival (TDOA) positioning has been an active research over the years due to its rich amounts of applications. Improving the quality of the TDOA measurements, especially under harsh scenarios, is crucial to achieve better localization accuracy and higher noise tolerance. Increasing the TDOA quality has been traditionally based on the statistical estimation point of view. Nevertheless, TDOA exhibits interesting algebraic properties, which when exploited can further enhance the measurements. This paper develops two algebraic properties of TDOA in the situation where multiple sources and sensors are present, such as sensor network and joint source-sensor localizations. The algebraic properties are new and have not appeared in the literature. They are in terms of the rank behaviors of two matrices that are formed by the TDOA themselves and their squared values. The development starts from an established property of Time of Arrival and applies determinant and vector space manipulations to obtain the concrete results. An example is included to illustrate the use of the algebraic properties for reducing the noise in TDOAs. [ABSTRACT FROM AUTHOR]

This paper deals with the bearing angle error (BAE) estimation problem associated with a one-axis gimballed ultrasonic seeker, which can be used as a low-cost sensor for mobile robot applications. Under the assumptions that the target has an ultrasonic transmitter and the gimbal control loops work properly, the moving target tracking reduces to an estimation problem of a piecewise constant BAE using real sinusoids measured by a receiver array mounted on the gimbal. To effectively cope with the real-time implementation issue, our estimation problem is formulated using linear robust state estimation and is resolved using the constrained robust weighted least squares method. Through experimental results obtained from a hardware-in-the-loop simulation (HILS) approach, it is demonstrated that the proposed scheme provides more reliable and consistent estimation performance than existing approaches. [ABSTRACT FROM PUBLISHER]

Phasor measurement units (PMUs) are playing an increasingly important role in wide-area monitoring and the control of power systems. PMUs allow synchronous real-time measurements of voltage, phase angle, and frequency from multiple remote locations in the grid, enabled by their ability to align to global positioning system (GPS) clocks. Given that this ability is vulnerable to GPS spoofing attacks, which have been confirmed easy to launch, in this paper, we propose a distributed real-time wide-area oscillation estimation approach that is robust to GPS spoofing on PMUs and their associated phasor data concentrators. The approach employs the idea of checking update consistency with histories and across distributed nodes and can tolerate up to one third of compromised nodes. It can be implemented in a completely decentralized architecture and in a completely asynchronous way. The effectiveness of the approach is confirmed by numerical simulations of the IEEE 68-bus power system models. [ABSTRACT FROM AUTHOR]

Line spectral estimation is the problem of recovering the frequencies and amplitudes of a mixture of a few sinusoids from equispaced samples. However, in a variety of signal processing problems arising in imaging, radar, and localization, we do not have access directly to such equispaced samples. Rather, we only observe a severely undersampled version of these observations through linear measurements. This paper is about such generalized line spectral estimation problems. We reformulate these problems as sparse signal recovery problems over a continuously indexed dictionary, which can be solved via a convex program. We prove that the frequencies and amplitudes of the components of the mixture can be recovered perfectly from a near-minimal number of observations via this convex program. This result holds provided the frequencies are sufficiently separated, and the linear measurements obey natural conditions that are satisfied in a variety of applications. [ABSTRACT FROM AUTHOR]

Maximum efficiency tracking is an important issue for wireless power transfer (WPT) system. Traditional maximum efficiency tracking method normally focuses on load impedance matching with fixed coupling condition. However, WPT system is a loosely coupling system, the coupling coefficient varies due to relative movement between the primary and secondary sides. Unknown to this variation may result in failure of the tracking. In this paper, a novel maximum efficiency tracking method is proposed with integrated dynamic coupling coefficient estimation. This method can take almost all requirements for maximum efficiency tracking into account including adaption for coupling coefficient and load variation, and output controllability. The tracking method is easy to implement because no additional circuitry or measurement is required. Experimental results have verified the correctness of the proposed coupling coefficient estimation method. And the maximum efficiency tracking results show the system can achieve a good performance against coupling coefficient and load variation with the proposed tracking method. [ABSTRACT FROM AUTHOR]

When using a camera to estimate the pose of a moving target, measurements may only be available intermittently, due to feature tracking losses from occlusions or the limited field of view of the camera. Results spanning back to the Kalman filter have demonstrated the utility of using a predictor to update state estimates when measurements are not available, but target velocity measurements or a motion model must be known to implement a predictor for image-based pose estimation. In this paper, a novel estimator and predictor are developed to simultaneously learn a motion model, and estimate the pose, of a moving target from a moving camera. A stability analysis is provided to prove convergence of the state estimates and function approximation without requiring the restrictive persistent excitation condition. Two experiments illustrate the performance of the developed estimator and predictor. One experiment involves a stationary camera observing a mobile robot with sporadic feature tracking losses, and a second experiment involves a quadcopter moving between two mobile robots on a road network. [ABSTRACT FROM AUTHOR]

Detecting periodicity in a short sequence is an important problem, with many applications across science and engineering. Several efficient algorithms have been proposed for this over the years. There is a wide choice available today in terms of the tradeoff between algorithmic complexity and estimation accuracy. In spite of such a rich history, one particular aspect of period estimation has received very little attention from a fundamental perspective. Namely, given a discrete time periodic signal and a list of candidate integer periods, what is the absolute minimum datalength required to estimate its integer period? Notice that the answer we seek must be a fundamental bound, i.e., independent of any particular period estimation technique. Common intuition suggests the minimum datalength as twice the largest expected period. However, this is true only under some special contexts. This paper derives the exact necessary and sufficient bounds to this problem. The above-mentioned question is also extended to the case of mixtures of periodic signals. First, a careful mathematical formulation discussing the unique identifiability of the component periods (hidden integer periods) is presented. Once again, a rigorous theoretical framework in this regard is missing in the existing literature but is a necessary platform to derive precise bounds on the minimum necessary datalength. The bounds given here are generic, that is, independent of the algorithms used. Specific algorithm-dependent bounds are also presented in the end for the case of dictionary-based integer period estimation reported in recent years. [ABSTRACT FROM AUTHOR]

The use of channel code is mandatory in current digital communication systems. It allows us to access the information on the receiver side despite the presence of noise. In this paper, we are interested in the blind identification of the parameters of an error correcting code from a received noisy data stream. The literature provides a large amount of contributions for this problem in the hard-decision case but few in the soft-decision case. It is well known that soft-decision methods allow significant gain in decoding techniques. Thence, we propose an algorithm which is able to identify the length of a code through a classification process from the bits likelihood values. It highlights a difference of behavior between an independent identically distributed sequence and an encoded one. This method does not rely on any a priori knowledge about the encoder involved. Indeed, the distribution of $n$ -length code words in an $n$ -dimensional space depends on the encoder characteristics. Some areas of this $n$ -dimensional space are left vacant because of the redundancy added by the encoder. Despite the presence of noise, it is still possible to detect this phenomenon. Furthermore, an adaptation of a collisions method based on the birthday paradox gives us access to an estimation of the code dimension. Finally, we investigate the performance of this estimation methods to show their efficiency. [ABSTRACT FROM AUTHOR]

Time-division duplex (TDD)-based massive MIMO systems rely on the reciprocity of the wireless propagation channels when calculating the downlink precoders based on uplink pilots. However, the effective uplink and downlink channels incorporating the analog radio front-ends of the base station (BS) and user equipments (UEs) exhibit non-reciprocity due to non-identical behavior of the individual transmit and receive chains. When the downlink precoder is not aware of such channel non-reciprocity (NRC), system performance can be significantly degraded due to the NRC-induced interference terms. In this paper, we consider a general TDD-based massive MIMO system where frequency-response mismatches at both the BS and UEs, as well as the mutual coupling mismatches at the BS large-antenna system all coexist and induce channel NRC. Based on the NRC-impaired signal models, we first propose a novel iterative estimation method for acquiring both the BS and UE side NRC matrices and then also propose an efficient NRC-aware downlink precoder design which utilizes the obtained estimates. Furthermore, an efficient pilot signaling scheme between the BS and UEs is introduced in order to facilitate executing the proposed estimation method and the NRC-aware precoding technique in practical systems. Comprehensive numerical results indicate substantially improved spectral efficiency performance when the proposed NRC estimation and NRC-aware precoding methods are adopted, compared to the existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Estimators of information theoretic measures, such as entropy and mutual information, are a basic workhorse for many downstream applications in modern data science. State-of-the-art approaches have been either geometric [nearest neighbor (NN)-based] or kernel-based (with a globally chosen bandwidth). In this paper, we combine both these approaches to design new estimators of entropy and mutual information that outperform the state-of-the-art methods. Our estimator uses local bandwidth choices of $k$ -NN distances with a finite $k$ , independent of the sample size. Such a local and data dependent choice ameliorates boundary bias and improves performance in practice, but the bandwidth is vanishing at a fast rate, leading to a non-vanishing bias. We show that the asymptotic bias of the proposed estimator is universal; it is independent of the underlying distribution. Hence, it can be precomputed and subtracted from the estimate. As a byproduct, we obtain a unified way of obtaining both the kernel and NN estimators. The corresponding theoretical contribution relating the asymptotic geometry of nearest neighbors to order statistics is of independent mathematical interest. [ABSTRACT FROM PUBLISHER]

This paper examines fundamental error characteristics for a general class of matrix completion problems, where the matrix of interest is a product of two a priori unknown matrices, one of which is sparse, and the observations are noisy. Our main contributions come in the form of minimax lower bounds for the expected per-element squared error for this problem under several common noise models. Specifically, we analyze scenarios where the corruptions are characterized by additive Gaussian noise or additive heavier-tailed (Laplace) noise, Poisson-distributed observations, and highly-quantized (e.g., one bit) observations, as instances of our general result. Our results establish that the error bounds derived in (Soni et al., 2016) for complexity-regularized maximum likelihood estimators achieve, up to multiplicative constants and logarithmic factors, the minimax error rates in each of these noise scenarios, provided that the nominal number of observations is large enough, and the sparse factor has (on an average) at least one non-zero per column. [ABSTRACT FROM PUBLISHER]

Interleaved acquisitions used in conventional triplex mode result in a tradeoff between the frame rate and the quality of velocity estimates. On the other hand, workflow becomes inefficient when the user has to switch between different modes, and measurement variability is increased. This paper investigates the use of power spectral Capon estimator in quantitative Doppler analysis using data acquired with conventional color flow imaging (CFI) schemes. To preserve the number of samples used for velocity estimation, only spatial averaging was utilized, and clutter rejection was performed after spectral estimation. The resulting velocity spectra were evaluated in terms of spectral width using a recently proposed spectral envelope estimator. The spectral envelopes were also used for Doppler index calculations using in vivo and string phantom acquisitions. In vivo results demonstrated that the Capon estimator can provide spectral estimates with sufficient quality for quantitative analysis using packet-based CFI acquisitions. The calculated Doppler indices were similar to the values calculated using spectrograms estimated on a commercial ultrasound scanner. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of robust clock phase offset estimation for the IEEE 1588 precision time protocol in the presence of unknown asymmetric network path delays. The presence of an unknown asymmetry between the path delays can lead to significant degradation in the performance of a phase offset estimation scheme. Assuming multiple master-slave communication paths are available, we first provide lower bounds on the best achievable performance for a phase offset estimation scheme in the presence of asymmetric master-slave communication paths. We then present a novel phase offset estimation scheme that employs the expectation-maximization algorithm to identify the asymmetric master–slave communication paths. After discarding information from these paths, we employ the minimax optimum vector location parameter estimator for estimating the phase offset. Simulation results are presented to show that the proposed phase offset estimation scheme exhibits performance close to the lower bounds in a wide variety of network scenarios. [ABSTRACT FROM PUBLISHER]

Multiple-input multiple-output (MIMO) radar enjoys the advantage of increased degrees-of-freedom and spatial diversity gain, but it cannot effectively resolves the targets closely spaced in the same angle cell (but different range cells). Frequency diverse array (FDA)-MIMO radar can handle this problem by exploiting its range-dependent beampattern. FDA-MIMO radar was, thus, suggested for range–angle estimation of targets. Nevertheless, it is necessary to provide theoretical performance analysis for such a relatively new radar technique. Since multiple signal classification (MUSIC) algorithm is widely adopted in most of the FDA-MIMO literature, this paper derives the Cramér–Rao lower bound and mean square error expressions in MUSIC-based range–angle estimation algorithms for a general FDA-MIMO radar. Furthermore, the corresponding range and angle resolution thresholds in target detection and localization are also derived. Numerical results verify that the FDA-MIMO indeed outperforms conventional MIMO radar in both range–angle estimation and resolution threshold performance. [ABSTRACT FROM AUTHOR]

We propose a structure of a semiparametric two-component mixture model when one component is parametric and the other is defined through linear constraints on either its distribution function or its quantile measure. Estimation of a two-component mixture model with an unknown component is very difficult when no particular assumption is made on the structure of the unknown component. A symmetry assumption was used in the literature to simplify the estimation. Such method has the advantage of producing consistent and asymptotically normal estimators, and identifiability of the semiparametric mixture model becomes tractable. Still, existing methods, which estimate a semiparametric mixture model have their limits when the parametric component has unknown parameters or the proportion of the parametric part is either very high or very low. We propose in this paper a method to incorporate a prior linear information about the unknown component in order to better estimate the model when existing estimation methods fail. This linear information is either translated by linear constraints, such as moment-type constraints or L-moments constraints (linear constraints over the quantile). The new method is based on $\varphi -$ divergences and has a non classical form since the minimization is carried over both arguments of the divergence. The resulting estimators are proved to be consistent and asymptotically normal under standard assumptions. We show that using the Pearson’s \chi ^{2} divergence our algorithm has a linear complexity when the constraints are moment-type. Simulations on univariate and multivariate mixtures demonstrate the viability and the interest of our novel approach. [ABSTRACT FROM AUTHOR]

This paper presents a new hybrid adaptive filter based on modified Gauss–Newton adaptive linear element (MGNA) for estimating the fundamental and harmonic phasors along with the frequency change of nonstationary power system signals useful in many application areas that include system control, digital relaying, state estimation, and also wide area systems. The proposed approach is used to minimize an objective function based on weighted square of the error using the MGNA. Moreover, the inverse of the Hessian matrix is computed assuming certain approximations to reduce the computational load and time consumption. Furthermore, it also uses recursive formulation using the estimated values from the previous time instant unlike the nonrecursive approaches, thereby exhibiting better performance in terms of accuracy and convergence. Besides, its simple structure makes it more suitable for real-time applications. In addition, the filter has been implemented on a field programmable gate array hardware and Xilinx 14.2 with the Sysgen software for the estimation of frequency, fundamental, and harmonic phasors of single and three-phase time-varying power system signals. [ABSTRACT FROM PUBLISHER]

This paper presents a new sensor management method for multitarget filtering, that is designed based on maximizing a measure of confidence in accuracy of the multitarget state estimate. Confidence of estimation is quantified by optimal subpattern assignment-based dispersion of the multitarget posterior about its statistical mean. Implementation of the algorithm for generic multitarget filters is presented. Simulation studies with labeled multi-Bernoulli filter demonstrate excellent performance in challenging sensor control scenarios. [ABSTRACT FROM AUTHOR]

In this paper, we provide recursion estimators in the case where a certain number of items are observed at the same time (but the time between two consecutive inspection times is not necessarily constant). This allows us to reduce the stored information. We provide sufficient conditions on the sampling scheme which ensures “good” asymptotic properties. Application to a real well-known data set, laser data, is at last proposed. [ABSTRACT FROM PUBLISHER]

This paper derives the Cramér–Rao lower bound (CRLB) for a general multichannel spaceborne synthetic aperture radar system for ground moving-target indication and provides a theoretical analysis of across-track velocity estimation for RADARSAT-2 and a fictitious satellite. The signal model assumes a deterministic target signal in Gaussian clutter and noise. In estimation theory, the CRLB provides a lower bound on the achievable variance of any unbiased estimator. An estimator that achieves this bound is called efficient; however, there is no guarantee that an efficient estimator can be found. Nonetheless, the theoretical variance of the efficient estimator provides a good measure of the capability of the system and serves as a valuable system performance validation tool. Even if an efficient estimator cannot be found, for radar systems, the CRLB provides a necessary, but not sufficient design baseline for measurement parameters, such as the number of subapertures for transmit and receive, power levels, pulse-repetition frequency, and so on. CRLBs for a limited number of system configurations for RADARSAT-2 and TerraSAR-X are presented by Cerutti-Maori et al. and Ender et al.; however, the complete analytical form of CRLB for a general system, which can be readily applied to an arbitrary antenna switching configuration, has not been previously derived. [ABSTRACT FROM PUBLISHER]

Polar codes have gained significant amount of attention during the past few years and have been selected as a coding scheme for the next generation of mobile broadband standard. Among decoding schemes, successive-cancellation list (SCL) decoding provides a reasonable tradeoff between the error-correction performance and hardware implementation complexity when used to decode polar codes, at the cost of limited throughput. The simplified SCL (SSCL) and its extension SSCL-SPC increase the speed of decoding by removing redundant calculations when encountering particular information and frozen bit patterns (rate one and single parity check codes), while keeping the error-correction performance unaltered. In this paper, we improve SSCL and SSCL-SPC by proving that the list size imposes a specific number of path splitting required to decode rate one and single parity check codes. Thus, the number of splitting can be limited while guaranteeing exactly the same error-correction performance as if the paths were forked at each bit estimation. We call the new decoding algorithms Fast-SSCL and Fast-SSCL-SPC. Moreover, we show that the number of path forks in a practical application can be tuned to achieve desirable speed, while keeping the error-correction performance almost unchanged. Hardware architectures implementing both algorithms are then described and implemented: It is shown that our design can achieve \mathbf 1.86 Gb/s throughput, higher than the best state-of-the-art decoders. [ABSTRACT FROM PUBLISHER]

In this paper, the problem of estimating complex-valued proportional covariance matrices is addressed. The obtained estimate generalizes the fixed point estimate to scaled packets of data, and is hence called the generalized fixed point estimate (GFPE). The statistical properties (bias, consistency, and asymptotical distributions) of the estimate are presented, and verified through simulations. As an example of application, a radar detection problem is considered. The GFPE is used in the well-known adaptive normalized matched filter (ANMF), and the obtained empirical probability of false alarms threshold curve is compared to the theoretical one. The ANMF is also used to compare the GFPE to the fixed point estimate. [ABSTRACT FROM PUBLISHER]

Distance measurement tasks in micromachine tools need to be performed with micrometer accuracy. For such tasks, frequency-modulated continuous-wave (FMCW) radars with a combination of frequency and phase evaluations are a good choice. However, the accuracy cannot be indefinitely increased as there are constraints on the target size and placement imposed by the limited space inside micromachines. This paper investigates the influence of target geometry and position on the accuracy of its range estimation using a W-band FMCW radar with a bandwidth of 25 GHz. A relation between target geometry and accuracy is established through the Cramér–Rao lower bound (CRLB). Based on the measurements of different targets, an optimal shape and size is proposed, which provides an average accuracy in the single digit micrometer range. Furthermore, the influence of different bandwidths on the accuracy is investigated. It is also demonstrated how the CRLB can be used to optimize the size of a target, when a certain accuracy is needed. In addition, antenna field regions are analyzed for suitable target placements. Finally, the radar system is implemented in a machine tool and measurements with accuracies in the micrometer range are carried out. [ABSTRACT FROM PUBLISHER]

Linear matching (LM) is a simple and effective method for solving image matching problems. In many cases, image matching problems are nonlinear due to involvement of the geometric transformations; therefore, an essential step for utilizing linear models for image matching is to linearize the geometric transformation matrices that introduce nonlinear terms into image matching problems. Existing LM methods usually use low-order transformations to artificially initialize a linear model. In this paper, we propose a residual-consensus driven LM algorithm that generalizes existing LM methods by allowing higher order transformations to automatically initialize a linear model. Based on the observation that transformation models generated from inlier subsets exhibit correlated behaviors (termed residual consensus hereafter), we develop a residual-consensus robust estimation algorithm to project the nontrivial linear transformation problem into a much smaller subspace, and thus enable efficient optimizations through linear programming. The experimental results on synthetic and real databases demonstrate the effectiveness and robustness of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

This paper proposes a method of estimating depth from two kinds of stereo images: color stereo images and infrared stereo images. An infrared dot pattern is projected on a scene by a projector so that infrared cameras can capture the scene textured by the dots and the depth can be estimated even where the surface is not textured. The cost volumes are calculated for the infrared and color stereo images for each frame and are extended in the time direction to define a spatiotemporal cost volume (st-cost volume). We also extend the cost volume filter in the time direction by modifying the cross-based local multipoint filter (CLMF) and applying it to the st-cost volumes in order to restrain flicker on the time-varying depth maps. To get a reliable cost volume, the infrared and color st-cost volumes are integrated into a single cost volume by selecting the cost of either the infrared or the color st-cost volumes according to the size of the adaptive kernel used for the CLMF. Then, a graphcut is executed on the cost volume in order to estimate the disparity robustly even when the baselines of the stereo cameras are set wide enough to ensure spatially high resolution in the depth direction and the shapes of blocks are deformed by the affine transformation. A 2D graphcut is executed on each scan line to reduce the processing time and memory consumption. We experimented with the proposed method using infrared color stereo data sets of scenes in the real world and evaluated its effectiveness by comparing it with other recent stereo matching methods and depth cameras. [ABSTRACT FROM PUBLISHER]

Diffusion adaptive networks tasked with solving estimation problems have attracted attention in recent years due to their reliability, scalability, resource efficiency, and resilience to node and link failure. Diffusion adaptation strategies that are based on the least-mean-squares algorithm can be nonrobust against impulsive noise corrupting the measurements. Impulsive noise can degrade stability and steady-state performance, leading to unreliable estimates. In previous work [“Robust adaptation in impulsive noise,” IEEE Trans. Signal Process., vol. 64, no. 11, pp. 2851–2865, Jun. 2016], a robust adaptive algorithm for stand-alone agents was developed, one that semi-parametrically estimates the optimal error nonlinearity jointly with the parameter of interest. Prior knowledge of the impulsive noise distribution was not assumed. In this paper, we extend the framework to solve the problem of robust distributed estimation by a network of agents. Challenges arise due to the coupling among the agents and the distributed nature of the problem. The resulting diffusion strategy is analyzed and its performance illustrated by numerical simulations. [ABSTRACT FROM PUBLISHER]

Accurate scale estimation of a target is a challenging research problem in visual object tracking. Most state-of-the-art methods employ an exhaustive scale search to estimate the target size. The exhaustive search strategy is computationally expensive and struggles when encountered with large scale variations. This paper investigates the problem of accurate and robust scale estimation in a tracking-by-detection framework. We propose a novel scale adaptive tracking approach by learning separate discriminative correlation filters for translation and scale estimation. The explicit scale filter is learned online using the target appearance sampled at a set of different scales. Contrary to standard approaches, our method directly learns the appearance change induced by variations in the target scale. Additionally, we investigate strategies to reduce the computational cost of our approach. Extensive experiments are performed on the OTB and the VOT2014 datasets. Compared to the standard exhaustive scale search, our approach achieves a gain of 2.5 percent in average overlap precision on the OTB dataset. Additionally, our method is computationally efficient, operating at a 50 percent higher frame rate compared to the exhaustive scale search. Our method obtains the top rank in performance by outperforming 19 state-of-the-art trackers on OTB and 37 state-of-the-art trackers on VOT2014. [ABSTRACT FROM PUBLISHER]

The objective of synthetic aperture radar (SAR) edge detection is the identification of contours across the investigated scene, exploiting SAR complex data. Edge detectors available in the literature exploit singularly amplitude and interferometric phase information, looking for reflectivity or height difference between neighboring pixels, respectively. Recently, more performing detectors based on the joint processing of amplitude and interferometric phase data have been presented. In this paper, we propose a novel approach based on the exploitation of real and imaginary parts of single-look complex acquired data. The technique is developed in the framework of stochastic estimation theory, exploiting Markov random fields. Compared to available edge detectors, the technique proposed in this paper shows useful advantages in terms of model complexity, phase artifact robustness, and scenario applicability. Experimental results on both simulated and real TerraSAR-X and COSMO-SkyMed data show the interesting performances and the overall effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

Nonselective gas sensor array has different sensitivities to different chemicals in which each gas sensor will also produce different voltage signals when exposed to an analyte with different concentrations. Therefore, the characteristics of cross sensitivities and broad spectrum of nonselective chemical sensors promote the fast development of portable and low-cost electronic nose (E-nose). Simultaneous concentration estimation of multiple kinds of chemicals is always a challengeable task in E-nose. Multilayer perceptron (MLP) neural network, as one of the most popular pattern recognition algorithms in E-nose, has been studied further in this paper. Two structures of single multiple inputs multiple outputs (SMIMO) and multiple multiple inputs single output (MMISO)-based MLP with parameters optimization in neural network learning processing using eight computational intelligence optimization algorithms are presented in this paper for detection of six kinds of indoor air contaminants. Experiments prove that the performance in accuracy and convergence of MMISO structure-based MLP are much better than SMIMO structure in concentration estimation for more general use of E-nose. [ABSTRACT FROM PUBLISHER]

Time-varying mixture models are useful for representing complex, dynamic distributions. Components in the mixture model can appear and disappear, and persisting components can evolve. This allows great flexibility in streaming data applications where the model can be adjusted as new data arrives. Fitting a mixture model with computational guarantees which can meet real-time requirements is challenging with existing algorithms, especially when the model order can vary with time. Existing approximate inference methods may require multiple restarts to search for a good local solution. Monte-Carlo methods can be used to jointly estimate the model order and model parameters, but when the distribution of each mixand has a high-dimensional parameter space, they suffer from the curse of dimensionality and and from slow convergence. This paper proposes a generative model for time-varying mixture models, tailored for mixtures of discrete-time Markov chains. A novel, deterministic inference procedure is introduced and is shown to be suitable for applications requiring real-time estimation, and the method is guaranteed to converge at each time step. As a motivating application, we model and predict traffic patterns in a transportation network. Experiments illustrate the performance of the scheme and offer insights regarding tuning of the algorithm parameters. The experiments also investigate the predictive power of the proposed model compared to less complex models and demonstrate the superiority of the mixture model approach for prediction of traffic routes in real data. [ABSTRACT FROM PUBLISHER]

In this paper, we introduce new gradient-based methods for image recovery from a small collection of spectral coefficients of the Fourier transform, which is of particular interest for several scanning technologies, such as magnetic resonance imaging. Since gradients of a medical image are much more sparse or compressible than the corresponding image, classical \ell 1 -minimization methods have been used to recover these relative differences. The image values can then be obtained by integration algorithms imposing boundary constraints. Compared with classical gradient recovery methods, we propose two new techniques that improve reconstruction. First, we cast the gradient recovery problem as a compressed sensing problem taking into account that the curl of the gradient field should be zero. Second, inspired by the emerging field of signal processing on graphs, we formulate the gradient recovery problem as an inverse problem on graphs. Iteratively reweighted \ell 1 recovery methods are proposed to recover these relative differences and the structure of the similarity graph. Once the gradient field is estimated, the image is recovered from the compressed Fourier measurements using least squares estimation. Numerical experiments show that the proposed approach outperforms the state-of-the-art image recovery methods. [ABSTRACT FROM AUTHOR]

The just noticeable difference (JND) in an image, which reveals the visibility limitation of the human visual system (HVS), is widely used for visual redundancy estimation in signal processing. To determine the JND threshold with the current schemes, the spatial masking effect is estimated as the contrast masking, and this cannot accurately account for the complicated interaction among visual contents. Research on cognitive science indicates that the HVS is highly adapted to extract the repeated patterns for visual content representation. Inspired by this, we formulate the pattern complexity as another factor to determine the total masking effect: the interaction is relatively straightforward with a limited masking effect in a regular pattern, and is complicated with a strong masking effect in an irregular pattern. From the orientation selectivity mechanism in the primary visual cortex, the response of each local receptive field can be considered as a pattern; therefore, in this paper, the orientation that each pixel presents is regarded as the fundamental element of a pattern, and the pattern complexity is calculated as the diversity of the orientation in a local region. Finally, considering both pattern complexity and luminance contrast, a novel spatial masking estimation function is deduced, and an improved JND estimation model is built. Experimental results on comparing with the latest JND models demonstrate the effectiveness of the proposed model, which performs highly consistent with the human perception. The source code of the proposed model is publicly available at http://web.xidian.edu.cn/wjj/en/index.html. [ABSTRACT FROM AUTHOR]

Fundamental relations between information and estimation have been established in the literature for the discrete-time Gaussian and Poisson channels. In this paper, we demonstrate that such relations hold for a much larger class of observation models. We introduce the natural family of discrete-time Lévy channels where the distribution of the output conditioned on the input is infinitely divisible. For Lévy channels, we establish new representations relating the mutual information between the channel input and output to an optimal expected estimation loss, thereby unifying and considerably extending results from the Gaussian and Poisson settings. We demonstrate the richness of our results by working out two examples of Lévy channels, namely the gamma channel and the negative binomial channel, with corresponding relations between information and estimation. Extensions to the setting of mismatched estimation are also presented. [ABSTRACT FROM AUTHOR]

Rotor magnet flux linkage is critical to monitor the state of the permanent magnet in a permanent magnet synchronous machine (PMSM). This paper proposes a novel online magnet flux-linkage estimation approach by using measured speed harmonics. The proposed approach is based on the PMSM's mechanical equation, so it is not influenced by magnetic saturation, inverter nonlinearity, and changes in machine parameters. The interaction of magnet flux linkage and harmonic current can produce torque harmonic, and thus speed harmonic. Therefore, we explore the use of speed harmonic for magnet flux-linkage estimation. At first, the torque and speed harmonic models are proposed. Then, the mathematical relationship between magnet flux linkage and speed harmonic is developed. Based on this model, a novel magnet flux-linkage estimation approach is proposed, which can eliminate the influence of the cogging torque and spatial harmonics in magnet flux-linkage estimation. The proposed approach is validated on a PMSM drive system under different loads and speeds. [ABSTRACT FROM PUBLISHER]

In this paper, we consider an OFDM radio link corrupted by oscillator phase noise in the receiver, namely the problem of estimating and compensating for the impairment. To lessen the computational burden and delay incurred onto the receiver, we estimate phase noise using only scattered pilot subcarriers, i.e., no tentative symbol decisions are used in obtaining and improving the phase noise estimate. In particular, the phase noise estimation problem is posed as an unconstrained optimization problem whose minimizer suffers from the so-called amplitude and phase estimation error. These errors arise due to receiver noise, estimation from limited scattered pilot subcarriers and estimation using a dimensionality reduction model. It is empirically shown that, at high signal-to-noise-ratios, the phase estimation error is small. To reduce the amplitude estimation error, we restrict the minimizer to be drawn from the so-called phase noise geometry set when minimizing the cost function. The resulting optimization problem is a nonconvex program. However, using the S-procedure for quadratic equalities, we show that the optimal solution can be obtained by solving the convex dual problem. We also consider a less complex heuristic scheme that achieves the same objective of restricting the minimizer to the phase noise geometry set. Through simulations, we demonstrate improved coded bit-error-rate and phase noise estimation error performance when enforcing the phase noise geometry. For example, at high signal-to-noise-ratios, the probability density function of the phase noise estimation error exhibits thinner tails which results in lower bit-error-rate. [ABSTRACT FROM PUBLISHER]

In this paper, we tackle the problem of stationary crowd analysis which is as important as modeling mobile groups in crowd scenes and finds many important applications in crowd surveillance. Our key contribution is to propose a robust algorithm for estimating how long a foreground pixel becomes stationary. It is much more challenging than only subtracting background because failure at a single frame due to local movement of objects, lighting variation, and occlusion could lead to large errors on stationary-time estimation. To achieve robust and accurate estimation, sparse constraints along spatial and temporal dimensions are jointly added by mixed partials (which are second-order gradients) to shape a 3D stationary-time map. It is formulated as an $L_0$ optimization problem. Besides background subtraction, it distinguishes among different foreground objects, which are close or overlapped in the spatio-temporal space by using a locally shared foreground codebook. The proposed technologies are further demonstrated through three applications. 1) Based on the results of stationary-time estimation, 12 descriptors are proposed to detect four types of stationary crowd activities. 2) The averaged stationary-time map is estimated to analyze crowd scene structures. 3) The result of stationary-time estimation is also used to study the influence of stationary crowd groups to traffic patterns. [ABSTRACT FROM AUTHOR]

Recent studies show that individuals in a social network can be divided into different groups of densely connected communities, and these individuals who bridge different communities, referred to as structural hole spanners, have great potential to acquire resources/information from communities and thus benefit from the access. Structural hole spanners are crucial in many real applications such as community detections, diffusion controls, viral marketing, etc. In spite of their importance, little attention has been paid to them. Particularly, how to accurately characterize the structural hole spanners and how to devise efficient yet scalable algorithms to find them in a large social network are fundamental issues. In this paper, we study the top-$k$ structural hole spanner problem. We first provide a novel model to measure the quality of structural hole spanners through exploiting the structural hole spanner properties. Due to its NP-hardness, we then devise two efficient yet scalable algorithms, by developing innovative filtering techniques that can filter out unlikely solutions as quickly as possible, while the proposed techniques are built up on fast estimations of the upper and lower bounds on the cost of an optimal solution and make use of articulation points in real social networks. We finally conduct extensive experiments to validate the effectiveness of the proposed model, and to evaluate the performance of the proposed algorithms using real world datasets. The experimental results demonstrate that the proposed model can capture the characteristics of structural hole spanners accurately, and the structural hole spanners found by the proposed algorithms are much better than those by existing algorithms in all considered social networks, while the running times of the proposed algorithms are very fast. [ABSTRACT FROM AUTHOR]

This paper presents an accurate initial pole position (IPP) estimation method for a surface mounted permanent magnet synchronous motor (PMSM) without the use of pole sensors. To estimate the IPP, the proposed method uses the concept of reference frame. In this method, three torque commands, separated by 45° electrically from each other, are applied to the PMSM. Using the reference torque commands, moving distances are measured and transformation matrix is applied to eliminate the load torque term. Finally, the IPP is determined using the secant method. The IPPs under no load and at specific loaded condition are measured. The result of the load condition is compared with the previously proposed two reference frame method. The proposed method gives better result under load condition, compared with the two reference frame method, which fails to give satisfactory result under load condition. [ABSTRACT FROM AUTHOR]